Abstract

: Artificial intelligence has been incorporated into modern agriculture to increase agricultural output and resource e ffi ciency. Utilizing deep learning, particularly convolutional neural networks, for recognizing and diagnosing plant diseases is tempting. In parallel, drone integration in precision agriculture has accelerated, providing new potential for crop monitoring, map creation, and targeted treatments. This study analyzes over 100 significant research articles published between 2018 and 2023, examining the interaction between drones and artificial intelligence in identifying plant diseases. We begin by explaining the value of sensor and drone technology in identifying plant diseases and carefully mapping the area. The various CNN architectures and drone-based approaches essential for precise illness detection and diagnosis are then highlighted in a thorough research review. Our research highlights how this combination can transform how plant diseases are managed completely. This study emphasizes the conceptual underpinnings of this new fusion, even if fulfilling this promise needs additional investigation. In conclusion, we expect changing research paths to direct improvements in this field and integrate AI, deep learning, drones, and plant pathology into a coherent framework with significant agricultural consequences.